Multiple-input-multiple-output (MIMO) systems are a telecommunication architecture where both the emitter and the receiver have several antennas, in order to improve communication performances. Different possibilities exist to make use of such architecture, including antenna diversity and spatial multiplexing.
Multiple-input-multiple-output (MIMO) systems using spatial multiplexing (SM) allows reaching higher performances (i.e. bitrate) than MIMO systems using diversity technique. However, the receiver's task of decoding the received signal is very complex.
In general, the receiver decodes the received signal by looking for a best estimate thanks to maximum likelihood (ML) detection methods. This task is very problematic for the receiver as the number of antennas increases due to the huge number of possible multiplexing combinations.
Some solutions known in the art which make use of ML criteria are based on QR decomposition technique. QR decomposition consists in transforming a matrix A into a product A=Q·R where Q is an orthogonal matrix and R is an upper triangular matrix.
Among the solutions based on ML decoding, one can mention sphere decoding and QRM-MLD.
The sphere decoding method is a search algorithm consisting in exploring all the lattice points inside a hypersphere centered at the received symbol vector. One of the most efficient known sphere decoding algorithms has been proposed in the article “A universal lattice code decoder for fading channels” by Emmanuel Viterbo and Joseph Boutros, in IEEE Transactions on Information Theory, vol. 45, no. 5, July 1999, the disclosure of which is incorporated by reference.
Sphere decoding allows decreasing the complexity, but it still suffers of major drawbacks: for instance, the algorithm is very dependent on the initial value for the search radius: if the search radius is chosen too small, there may be no solution in the hypersphere, but if the search radius is chose too large, the number of points to explore may become too large and the algorithm will encounter the same issue as ML-based detection algorithms.
Furthermore, the algorithm cannot be parallelized on a hardware platform, preventing it to be efficiently used in a real-time high data rate context. Also, its complexity is very dependent on the signal-to-noise ratio (SNR) and on the channel modeling parameters used for implementing the algorithm.
Another set of solutions known in the art are based on the so-called “QRM-MLD” techniques, i.e. QR Decomposition with order M Maximum Likelihood Decoding”. Such a method has for example been described in the article “Joint Data Detection and Channel Estimation of OFDM Systems” by Tao Cui and Chintha Tellambura, in IEEE Transactions on communications, vol. 54n no. 4, April 2006, the disclosure of which is incorporated by reference, or in the article “Likelihood function for QRM-MLD suitable for soft-decision turbo decoding and its performance for OFCDM MIMO multiplexing in multipath fading channel”, by H. Kawai, K. Higuichi, N. Maeda et al., in IEICE Transactions on Communications, E88-B (1), 47-57, the disclosure of which is incorporated by reference.
Compared with Sphere Decoding, QRM-MLD techniques, which only keeps the best M candidates for the next level search, has fixed throughput and is suitable for pipelined hardware implementation. In terms of result accuracy, it achieves comparable performance with ML-based detection algorithms.
However, it involves a very high complexity, especially for higher modulation schemes like 256 QAM (Quadrature Amplitude Modulation). Also, like the Sphere Decoding solution, this algorithm cannot be implemented on highly parallel hardware architecture and is thus not suitable for real-time high data rate transmission.